Owing to high degree of flexibility, Artificial Neural Networks (ANN) can be used to model nonlinear channel estimation phenomenon. An ANN-based channel estimation technique and relay selection scheme was proposed as an alternate to Fuzzy Logic Controller (FLC) based channel estimation system. ANN's learning property is fully exploited to decipher the deteriorated symbols through extreme faded networks. Similar to FLC channel estimation techniques, this technique is found to be more effective in increasing bandwidth. Simulated results for Multi-input Multi-output (MIMO) of 2×2 and 4×4 using Additive White Gaussian Noise (AWGN) and Rayleigh channel in terms of quantity (Mbps) compared with Signal to Noise Ratio (SNR) over a choice of 0 to 30 dB. This illustrates the effectiveness of the knowledge proficiency of ANNs is 92.19% of AWGN and 94.62% of Rayleigh whereas for FLC is 70.34% of AWGN and 75.51% of Rayleigh channel.
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